Enhancing the Value of Machine Data with the Fourth V: Visualization

As machines increasingly are fitted with internet and other network access, enterprises will be able to capture and increasingly expected to respond to more customer data than ever before. Machine-to-machine (M2M) network connections—this so-called “Internet of Things”—is positioned to become the next source of major competitive advantage. Axeda, a major connected-product service technology, recognized this advantage and gave it an enterprise slant by naming it the “Internet of Corporate Things.” GE calls it the “Industrial Internet.” Whatever you call it, M2M is turning out to be the poster child for big data’s “Three Vs”: Volume, Velocity and Variety.

Today’s machines, highways, smart grids and even people are generating a tremendous amount of data which, if captured and transformed into intelligence for real-time answers, can spark a radical shift in productivity and efficiency for today’s data-driven organizations. Sensor technology, M2M connectivity and intelligence, all delivered as it happens, are opening the door for organizations to get a better understanding of every operation and customer across their entire organization. This is what enterprises are calling “real-time.” They want insight into their business as events are unfolding, not a day, week or month later.

M2M sensor data is data in motion. Organizations that use M2M data are interested in real-time insight across all their assets. That’s a lot of data, from a lot of systems, that is continuously changing. It’s of huge value to have real-time insight into the health of all your systems. Why? Because the sooner you catch issues, the greater impact you can have on cost, profit, customer satisfaction and even lives.

Context Key to “Data in Motion”

But organizations are realizing that just looking at sensor data as it’s happening may be missing important contextual value by not incorporating historical information and data from other important systems. Enterprises are beginning to understand the many benefits of combining machine sensor data with other enterprise systems, like ERP, CRM, trouble ticketing, and service lifecycle management into connected, real-time visual dashboards. For example, an overheated engine that exceeds a threshold may generate an alarm, simultaneously generate a trouble ticket, and update the customer’s CRM and service records. Just this simple real-world example can save companies millions of dollars by avoiding the high costs of unscheduled maintenance visits.

This new capability owes thanks to all the technology advancements that have brought down the costs of making M2M available to the mainstream. Back in 1998, Sun Microsystems introduced a Java technology called Jini (http://en.wikipedia.org/wiki/Jini) which was designed to be a distributed system where anything that runs Java could be connected and discovered. There was talk about the “connected refrigerator,” where refrigerators would sense what was inside and could notify the supermarket when items needed to be replenished. Even though the “everything connected” concept was appealing, it was not practical at that time due to technology and network costs. But now, with GPS and monitoring sensors becoming inexpensive and ubiquitous, the opportunities seem endless. Imagine the physical world seamlessly interacting with the software world. That’s the business value of this connectivity, but there’s work to done to get that value to the business user.

That work includes harvesting data, analyzing it in context, and making adjustments in real-time. The same data-driven approach that gives us dynamic pricing on Amazon and customized recommendations on Foursquare has already started to make wind turbines more efficient and thermostats more responsive. It may soon obviate humans as drivers and help blast furnaces anticipate changes in electricity prices.